The REU program Mathematical, Statistical, and Computational Methods in the Life Sciences is an eight-week intensive program that actively engages undergraduate students in research projects designed to introduce them to mathematical, statistical, and computational methods that are used in the study of life science questions. Students work individually or in groups of two or three, under the supervision and guidance of three faculty mentors. The research projects cover a wide array of life science applications on the dynamics of populations, epidemics, organisms, cells, and proteins. The objectives of the program are to engage undergraduate students, especially those from underrepresented groups and from academic institutions with limited STEM research opportunities, in innovative research projects, to expose them to active research environments, and to provide them with the necessary technical skills to do independent research. Through a series of educational and social activities, REU participants have opportunities to enhance their professional development and to form a network of partners among the participants and collaborators in the program. Faculty continue to mentor the REU participants after the 8-week program to guide them in writing their results for publication and to assist them as they transition into graduate school. The goal of the program is to motivate and to inspire the REU participants to continue graduate study in mathematics, statistics, or a related field and to pursue academic or other research careers in STEM disciplines.

The research projects of the REU program introduce undergraduate students to mathematical, statistical, and computational methods that enable them to pursue independent research on current questions in the life sciences. Under the guidance of experienced faculty mentors, the student research projects will involve (1) development of new stochastic models and computational methods to address biological questions on emerging diseases or species invasion; (2) new methods in time-nonhomogeneous processes to determine times at which zoonotic transmission risk is greatest; (3) development of numerical methods, especially the primal-dual weak Galerkin finite element methods, with applications in Nernst-Planck model arising from life sciences; (4) development of optimization methods with the mathematical and statistical constraints inherent to the biological and chemical aspects of the structural alignment of protein binding sites; and (5) new insights on the effect of community structure and nutrient cycling when aquatic food webs are subject to stoichiometric constraints. The research projects will build on current results and aim to contribute to new mathematical, statistical, and computational methods, algorithm design, analysis, data collection, and implementation to address important complex problems in the life sciences. The results from the research projects will be disseminated through student participation in conferences and workshops, and through publications in mathematical, statistical, and biological journals. In addition, code packages that are developed will be available on the program's website.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Stefaan De Winter
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Texas Tech University
United States
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